An integrated knowledge mobilization approach to substance use health
Bibliographic record
Abstract
The Canadian Centre on Substance Use and Addiction (CCSA) has a mandate to provide national leadership in evidence-informed analysis and knowledge mobilization to advance solutions that reduce substance-related harms. Doing this work effectively requires an understanding of the needs, priorities, perspectives and ideologies of multiple groups. Partnerships across various sectors support a full understanding and acknowledgement of the systems that create differential health outcomes for individuals and communities. CCSA has developed an integrated knowledge mobilization model to guide our work in supporting better substance use health outcomes. Our model begins by understanding the context a particular need (for example, research question and practice improvement) is occurring within. This involves engaging key partners with multiple viewpoints to understand the current situation, constraints and opportunities, including barriers to care, social and structural determinates of health and community strengths and assets. Based on this, the steps that follow involve determining the appropriate action and CCSA's unique role to respond in alignment with partner and community priorities to advance solutions within the given context. This leads to an iterative process of generating and mobilizing knowledge. This integrated and collaborative approach ensures that responses are relevant to the identified knowledge gap, that recommendations reflect partners' realities and that our efforts will achieve impact while minimizing the risk of harm. Through an iterative process of generating and mobilizing knowledge (for example, supporting the scale and spread of innovations, developing new tools and generating or tailoring evidence for a specific audience/context/substance/setting, among others), outputs such as increased awareness, knowledge, use of information and strengthened capacity occur. Together, these efforts contribute to the outcome of a healthier society for people living in Canada, where multiple forms of evidence advance substance use health. Meaningful engagement of partners and evaluation of our efforts are ingrained throughout the model to ensure our work has the intended effects. We share our approach for the consideration of other organizations (in the space of substance use health and otherwise) to engage partners in the development of evidence and other resources that can drive impactful programs, practice and policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".